Accelerated CD8+ T cell maturation in infants with perinatal HIV infection.

In perinatal HIV infection, early antiretroviral therapy (ART) initiation is recommended but questions remain regarding infant immune responses to HIV and its impact on immune development. Using single cell transcriptional and phenotypic analysis we evaluated the T cell compartment at pre-ART initiation of infants with perinatally acquired HIV from Maputo, Mozambique (Towards AIDS Remission Approaches cohort). CD8+ T cell maturation subsets exhibited altered distribution in HIV exposed infected (HEI) infants relative to HIV exposed uninfected infants with reduced naive, increased effectors, higher frequencies of activated T cells, and lower frequencies of cells with markers of self-renewal. Additionally, a cluster of CD8+ T cells identified in HEI displayed gene profiles consistent with cytotoxic T lymphocytes and showed evidence for hyper expansion. Longitudinal phenotypic analysis revealed accelerated maturation of CD8+ T cells was maintained in HEI despite viral control. The results point to an HIV-directed immune response that is likely to influence reservoir establishment.

Opioids exacerbate inflammation in people with well-controlled HIV.

Introduction: People with HIV (PWH) are known to have underlying inflammation and immune activation despite virologic control. Substance use including opioid dependence is common in this population and is associated with increased morbidity and reduced lifespan. The primary objective of the present study termed opioid immunity study (OPIS), was to investigate the impact of chronic opioids in PWH. Methods: The study recruited people with and without HIV who had opioid use disorder (OUD). Study participants (n=221) were categorized into four groups: HIV+OP+, n=34; HIV-OP+, n=66; HIV+OP-, n=55 and HIV-OP-, n=62 as controls. PWH were virally suppressed on ART and those with OUD were followed in a syringe exchange program with confirmation of OP use by urine drug screening. A composite cytokine score was developed for 20 plasma cytokines that are linked to inflammation. Cellular markers of immune activation (IA), exhaustion, and senescence were determined in CD4 and CD8 T cells. Regression models were constructed to examine the relationships of HIV status and opioid use, controlling for other confounding factors. Results: HIV+OP+ participants exhibited highest inflammatory cytokines and cellular IA, followed by HIV-OP+ for inflammation and HIV+OP- for IA. Inflammation was found to be driven more by opioid use than HIV positivity while IA was driven more by HIV than opioid use. In people with OUD, expression of CD38 on CD28-CD57+ senescent-like T cells was elevated and correlated positively with inflammation. Discussion: Given the association of inflammation with a multitude of adverse health outcomes, our findings merit further investigations to understand the mechanistic pathways involved.

CyTOF® for the Masses.

Mass cytometry has revolutionized immunophenotyping, particularly in exploratory settings where simultaneous breadth and depth of characterization of immune populations is needed with limited samples such as in preclinical and clinical tumor immunotherapy. Mass cytometry is also a powerful tool for single-cell immunological assays, especially for complex and simultaneous characterization of diverse intratumoral immune subsets or immunotherapeutic cell populations. Through the elimination of spectral overlap seen in optical flow cytometry by replacement of fluorescent labels with metal isotopes, mass cytometry allows, on average, robust analysis of 60 individual parameters simultaneously. This is, however, associated with significantly increased complexity in the design, execution, and interpretation of mass cytometry experiments. To address the key pitfalls associated with the fragmentation, complexity, and analysis of data in mass cytometry for immunologists who are novices to these techniques, we have developed a comprehensive resource guide. Included in this review are experiment and panel design, antibody conjugations, sample staining, sample acquisition, and data pre-processing and analysis. Where feasible multiple resources for the same process are compared, allowing researchers experienced in flow cytometry but with minimal mass cytometry expertise to develop a data-driven and streamlined project workflow. It is our hope that this manuscript will prove a useful resource for both beginning and advanced users of mass cytometry.

Uncertainty-aware mixed-variable machine learning for materials design.

Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods. Bayesian optimization (BO) employs uncertainty-aware machine learning models to select promising designs to evaluate, hence reducing the cost. However, BO with mixed numerical and categorical variables, which is of particular interest in materials design, has not been well studied. In this work, we survey frequentist and Bayesian approaches to uncertainty quantification of machine learning with mixed variables. We then conduct a systematic comparative study of their performances in BO using a popular representative model from each group, the random forest-based Lolo model (frequentist) and the latent variable Gaussian process model (Bayesian). We examine the efficacy of the two models in the optimization of mathematical functions, as well as properties of structural and functional materials, where we observe performance differences as related to problem dimensionality and complexity. By investigating the machine learning models' predictive and uncertainty estimation capabilities, we provide interpretations of the observed performance differences. Our results provide practical guidance on choosing between frequentist and Bayesian uncertainty-aware machine learning models for mixed-variable BO in materials design.

Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges.

Organic molecules and polymers have a broad range of applications in biomedical, chemical, and materials science fields. Traditional design approaches for organic molecules and polymers are mainly experimentally-driven, guided by experience, intuition, and conceptual insights. Though they have been successfully applied to discover many important materials, these methods are facing significant challenges due to the tremendous demand of new materials and vast design space of organic molecules and polymers. Accelerated and inverse materials design is an ideal solution to these challenges. With advancements in high-throughput computation, artificial intelligence (especially machining learning, ML), and the growth of materials databases, ML-assisted materials design is emerging as a promising tool to flourish breakthroughs in many areas of materials science and engineering. To date, using ML-assisted approaches, the quantitative structure property/activity relation for material property prediction can be established more accurately and efficiently. In addition, materials design can be revolutionized and accelerated much faster than ever, through ML-enabled molecular generation and inverse molecular design. In this perspective, we review the recent progresses in ML-guided design of organic molecules and polymers, highlight several successful examples, and examine future opportunities in biomedical, chemical, and materials science fields. We further discuss the relevant challenges to solve in order to fully realize the potential of ML-assisted materials design for organic molecules and polymers. In particular, this study summarizes publicly available materials databases, feature representations for organic molecules, open-source tools for feature generation, methods for molecular generation, and ML models for prediction of material properties, which serve as a tutorial for researchers who have little experience with ML before and want to apply ML for various applications. Last but not least, it draws insights into the current limitations of ML-guided design of organic molecules and polymers. We anticipate that ML-assisted materials design for organic molecules and polymers will be the driving force in the near future, to meet the tremendous demand of new materials with tailored properties in different fields.

Data-Driven Methods for Advancing Precision Oncology.

PURPOSE OF REVIEW: This article discusses the advances, methods, challenges, and future directions of data-driven methods in advancing precision oncology for biomedical research, drug discovery, clinical research, and practice. RECENT FINDINGS: Precision oncology provides individually tailored cancer treatment by considering an individual's genetic makeup, clinical, environmental, social, and lifestyle information. Challenges include voluminous, heterogeneous, and disparate data generated by different technologies with multiple modalities such as Omics, electronic health records, clinical registries and repositories, medical imaging, demographics, wearables, and sensors. Statistical and machine learning methods have been continuously adapting to the ever-increasing size and complexity of data. Precision Oncology supportive analytics have improved turnaround time in biomarker discovery and time-to-application of new and repurposed drugs. Precision oncology additionally seeks to identify target patient populations based on genomic alterations that are sensitive or resistant to conventional or experimental treatments. Predictive models have been developed for cancer progression and survivorship, drug sensitivity and resistance, and identification of the most suitable combination treatments for individual patient scenarios. In the future, clinical decision support systems need to be revamped to better incorporate knowledge from precision oncology, thus enabling clinical practitioners to provide precision cancer care. SUMMARY: Open Omics datasets, machine learning algorithms, and predictive models have enabled the advancement of precision oncology. Clinical decision support systems with integrated electronic health record and Omics data are needed to provide data-driven recommendations to assist clinicians in disease prevention, early identification, and individualized treatment. Additionally, as cancer is a constantly evolving disorder, clinical decision systems will need to be continually updated based on more recent knowledge and datasets.